The embodiments described herein relate generally to image processing, and more particularly, to detecting luggage from imaging data generated by a computed tomography (CT) imaging system.
Known luggage scanning systems, such as explosives detection systems (EDS) that perform computed tomography (CT) scanning, are designed to scan a continuous stream of luggage and other objects to provide adequate throughput for travelers at an airport, for example. As a result, techniques have been developed to delineate the beginning and the end of each object, such as a travel bag, in the continuous scan of luggage. Traditional methods for this object boundary detection rely on multiple optical sensors to find the edges of luggage items within a scan tunnel of the luggage scanning system. However, drawbacks of such systems persist because the employed optical sensors may have gaps in sensing coverage or may cover only a small portion of the scan tunnel. Some conventional scanning systems use imaging data from X-ray detectors to identify the leading and the trailing edges of a piece of luggage.
However, if an X-ray detector is triggered by a spurious impetus, such as a vibration in the system, an object stuck to the conveyor belt, or other external causes, it is difficult to correlate those triggers back to a physical object (e.g., a bag) in the system. Furthermore, the optical sensors and X-ray detectors can indicate different spatial locations of the leading and of the trailing edges of a particular bag. This disagreement between different spatial locations of the bag edges prompts situations where a bag is deemed to be present by one of the two systems and not by the other system, leading to the detection of false positives (“phantom bags”) and false negatives (“chopped bags”). Moreover, because conventional detection occurs in a non-reconstruction domain of the raw imaging data (i.e., non-human readable), it remains difficult for a user to visually interpret the occurrence of a false positive or a false negative.